Economic Regions In China Research Articles

Source Apportionment and Health Risk Assessment of Heavy Metals in Endemic Tree Species in Southern China: A Case Study of Cinnamomum camphora (L.) Presl.

As a developed economic region in China, the problem of heavy metals (HMs) pollution in the Yangtze River Delta has become increasingly prominent. As an important evergreen broad-leaved tree species in southern China, the camphor tree cannot only be used as a street tree but also its various tissues and organs can be used as raw materials for Chinese herbal medicine. In order to explore whether heavy metal contamination in the region threatens the safety of camphor trees as pharmaceutical raw materials, we collected the bark and leaves of the tree most commonly used for pharmaceuticals in Yixing City. Based on the determination of HMs content, the health risks after human intake are evaluated, the sources and contributions of HMs are analyzed, and then the health risks of pollution sources are spatially visualized. The results showed that under the influence of human activities, the camphor trees in the study area had obvious enrichment of HMs, and the over-standard rate of Pb in the bark was as high as 90%. The non-carcinogenic risks of bark and leaves are acceptable, but the carcinogenic risks are not acceptable. The bark had the highest average carcinogenic risk, approaching six times the threshold. The results of Pb isotope ratio analysis showed that the average contribution rate of industrial activities to HMs in camphor trees in the study area was the highest, reaching 49.70%, followed by fossil fuel burning (37.14%) and the contribution of natural sources was the smallest, only 13.16%. The locations of the high-risk areas caused by the three pollution sources in the study area are basically similar, mainly concentrated in the northwest, northeast, and southeast, which are consistent with the distribution of industries and resources in the study area. This study can provide a reference for the precise prevention of HMs pollution of camphor and the safe selection of its pharmaceutical materials.

Multidimensional effects of urbanization on PM2.5 concentration in China.

Recently, the contradiction between urbanization and the air environment has gradually attracted attention. However, most existing studies have explored the impact of single urbanization factors, such as population, the economy, or land, on PM2.5 and ignored the impact of multidimensional urbanization on PM2.5 concentration. Moreover, the heterogeneity in the mechanisms responsible for the PM2.5 concentration caused by multidimensional urbanization has not been thoroughly studied in different regions in China. Therefore, we investigate the spatial-temporal evolution characteristics of PM2.5 concentration in China during 1998-2019 by spatial analysis and dynamic panel models based on the environmental Kuznets curve (EKC). Then, we study the effects of multidimensional urbanization on PM2.5 concentration, and analyze the dominant factors in China's eight economic regions. During the study period, the PM2.5 concentration in China fluctuated before 2013 and gradually decreased thereafter. The PM2.5 concentration has significant regional differences in China. Spatially, the PM2.5 concentration is higher in the north than in the south and higher in the east than in the west. Additionally, there is a significant spatial spillover effect. Both population urbanization and economic urbanization show an inverted U-shaped relationship with PM2.5 concentration in China, which is consistent with the classical EKC theory. Due to other socioeconomic factors, the PM2.5 concentration tends to decrease linearly with increasing land urbanization rate. The effects of urbanization on the PM2.5 concentration in the eight economic regions in China show significant differences. The effect of land urbanization on the PM2.5 concentration is dominant in the Middle Yangtze River region, that of economic urbanization is dominant in northwestern China, and that of population urbanization is dominant in the remaining regions in China.

Research on Regional Collaborative Network Innovation and Enterprises’ Innovation Performance in the Sustainable Development of Yangtze River Delta, China

As one of the most dynamic and sustainable economic regions in China, Yangtze River Delta has been forming an innovation cluster of high-quality development. To explore the driving force during the integration of Yangtze River Delta, this article constructs a driving force model and makes an empirical study by the structural equation modeling method. The model proves that heterogeneity knowledge will positively affect innovation, that innovation performance has a strong positive impact on regional collaborative network innovation, that regional collaborative network innovation exerts a positive effect on knowledge spillover, and that organizational learning can affect innovation performance positively. Based on the above research results, some recommendations are proposed. Those recommendations consist of: (a) to forge a dynamic sustainable and innovative regional collaborative network system lay a foundation of high-quality development; (b) while the two driving forces of heterogeneity knowledge and organizational learning activated, the indices of enterprise innovation performance should be optimized.

Changes in carbon emission performance of energy-intensive industries in China.

As the major energy consumers, energy-intensive industries are the key players in achieving carbon emission reduction targets. The paper builds a super slack-based model (SBM) considering this undesirable output and calculates the carbon emission efficiency. Then, the meta-frontier Malmquist-Luenberger productivity index (MF-MLPI) is constructed to dynamically analyze the growth rate changes of the carbon emission efficiency and the regional differences in energy-intensive industries. Furthermore, the carbon emission reduction potential of the energy-intensive industries in various economic regions of China is discussed, and the conclusions are as follows: there is a big difference in the carbon emission technology gap ratios (TGRs) of the energy-intensive industries in different economic regions; the growth rate of the carbon emission efficiency of energy-intensive industries shows a trend of first declining and then slowly recovering, while the carbon reduction potential generally shows a trend of decreasing and then rising; and the carbon emission reduction potential in the eastern region keeps decreasing.

Research on carbon emission differences decomposition and spatial heterogeneity pattern of China's eight economic regions.

To explore the sources of regional carbon emission differences and the evolution characteristics of spatial heterogeneity pattern, this paper first calculates the corresponding carbon emissions according to the relevant statistical data of eight economic regions in China from 2005 to 2019. It analyzes the overall differences and temporal and spatial evolution characteristics of regional carbon emissions combined with the visualization method of GIS. Then, the total carbon emission difference is decomposed by the Theil index to find out the primary sources affecting the regional carbon emission difference. Finally, the driving factors affecting the spatial heterogeneity pattern of regional carbon emissions are studied with the help of the Geodetector method. The results show that (1) significant differences in carbon emissions among China’s eight economic regions. The contribution rate of inter-regional and intra-regional differences of carbon emissions in different regions to the overall carbon emission difference is diverse. (2) Regional carbon emissions are affected by single driving factors and the interaction of two driving factors. The interaction has an increasing impact on the determinant of regional carbon emission spatial differentiation. (3) The factor detection results and interaction detection results, respectively, show that the level of energy consumption, industrialization, and technological development has always been the main driving factors affecting the spatial heterogeneity pattern of regional carbon emissions, and the critical interaction factors have multiple spatial superposition interaction effects. Therefore, regional carbon emission reduction should consider the national strategic objectives and own regional characteristics and implement differentiated emission reduction schemes.

The Impact of Population Ageing on Rural Revitalization- Evidence from China's Yangtze River Economic Belt

The Yangtze River Economic Belt is an important demographic and economic region in China, and an empirical study of this region can provide a good understanding of the actual situation in China. This paper uses the panel data of the Yangtze River Economic Belt from 2011 to 2020 and establishes a comprehensive evaluation index system for rural revitalization. Drawing conclusions after empirical research using the entropy value method and panel Tobit model: (1) During the decade from 2011 to 2020, the level of rural revitalization in all 11 provinces and cities of the Yangtze River Economic Belt has been improving, and the growth rates are not significantly different. (2) In general, population Ageing has promoted the development of rural revitalization in the Yangtze River Economic Belt. (3) The regions' empirical results show that the population Ageing's promoting effect on rural revitalization is only produced in downstream Jiangsu, Zhejiang and Shanghai while inhibiting the effect on the middle and upstream regions.

Effect of Vegetation Coverage on the Temporal and Spatial Distribution of PM2.5 Concentration in China's Eight Major Economic Regions from 1998 to 2016

The study researched the relationship between vegetation cover and PM2.5 pollution. The raster NDVI dataset from 1998 to 2016 were reclassified into low, medium, and high vegetation coverage area, and the corresponding PM2.5 concentration in eight economic regions in China were then calculated. On this basis, the temporal and spatial characteristics of PM2.5 pollution were analyzed and Pearson correlation coefficient was used to explore its correlation with NDVI landscape pattern indexes separately from landscape and class level NDVI. The preliminary results showed that:①The northern, eastern, southern coastal, middle reaches of the Yangtze River, and the northeast economic zones have relatively low vegetation coverage in areas with relatively serious PM2.5 pollution. However, the middle reaches of the Yellow River, the southwestern and the Northwestern Economic Zones in areas with relatively low vegetation coverage showed lighter PM2.5 pollution. ②PM2.5 increased in most areas between 1998 and 2016. ③A significant correlation between PM2.5 and NDVI landscape pattern indexes was not found for all areas. ④Therefore, the impacts of the landscape shape index(LSI), percent of landscape(PLAND), number of patches(NP), largest patch index(LPI), and aggregation index(AI) on PM2.5 are heterogeneous.

Systematic review and meta-analysis: Prevalence of diabetes among patients with tuberculosis in China.

Tuberculosis (TB) and diabetes mellitus (DM) are both significant public health problems in China. Results of previous studies on the prevalence of DM among TB patients in China are inconsistent. We conducted this systematic review and meta-analysis to estimate the overall prevalence of DM among TB patients in China. We systematically searched Excerpta Medica Database, PubMed, Global Health, Ovid MEDLINE(R), Chinese Biomedical Literature Service System, and China Online Journals -Health & Medicine and included all observational studies reporting prevalence of DM among TB patients in China. The Cochran's Q-statistic and were used to test heterogeneity. Finally, a random-effect model meta-analysis was conducted to estimate the pooled prevalence of DM among TB patients in China using R studio. We screened 7043 articles and identified 43 eligible studies. The pooled prevalence was 7.8% (95% prediction interval 1.6-30.5, =99.2%). The highest prevalence was observed in Northeast China (21.9%) among four economic regions of China, followed by East Coast (8.3%), Western China (5.9%), with Central China having the lowest prevalence (5.1%). Higher prevalence was observed in urban (10.1%) than in rural (7.8%) areas, and in hospital-based (9.0%) than in population-based studies (6.9%). This study suggests that the prevalence of DM among TB patients in China aligns with the overall DM prevalence among the public in China. Public health strategies to reduce the burden of TB-DM comorbidity and inequity are needed.

Economic growth, electricity consumption, and urbanization in China: A tri-variate investigation using panel data modeling from a regional disparity perspective

This paper aims to examine the causal relations between economic growth, electricity consumption, and urbanization indicators. Heterogeneous panel data techniques are performed to help shed light on the importance of the heterogeneity among three economic regions within China from 2000 to 2017. Our results show that the three indicators are all stationary and cointegrated after the first difference. Heterogeneity of the Granger causality is found between these three variables varies across the eastern, central, and western economic regions in China. In the long run, the eastern region presents a similar trend as the national level that long-run Granger causality of the three variables is found. Conversely, there is a unidirectional long-run causal relationship from economic growth to electricity consumption in the central region, while there is a bi-directional long-run causal relationship between economic growth and electricity consumption in the western region in China. Our empirical findings highlight the “urbanization ladder” effect which implies that as development, urbanization factor in the eastern region may play a promotion role in the tri-variate system when the urbanization reaches a higher level, while it may lack the influence in the central and western regions of China. From a regional perspective, it is further indicated that the improvement of urbanization can be practical measures in the central and western parts of China in the long run.

Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19

The Central Plains Economic Region (CPER) located along the transport path to the Beijing-Tianjin-Hebei area has experienced severe PM2.5 pollution in recent years. However, few modeling studies have been performed on the sources of PM2.5, especially the impacts of emission reduction strategies. In this study, the Nested Air Quality Prediction Model System (NAQPMS) with an online tracer-tagging module was adopted to investigate source sectors of PM2.5 and a series of sensitivity tests were conducted to investigate the impacts of different sector-based mitigation strategies on PM2.5 pollution. The response surfaces of pollutants to sector-based emission changes were built. The results showed that resident-related sector (resident and agriculture), fugitive dust, traffic and industry emissions were the main sources of PM2.5 in Zhengzhou, contributing 49%, 19%, 15% and 13%, respectively. Response surfaces of pollutants to sector-based emission changes in Henan revealed that the combined reduction of resident-related sector and industry emissions efficiently decreased PM2.5 in Zhengzhou. However, reduced emissions in only the Henan region barely satisfied the national air quality standard of 75 μg/m3, whereas a 50%–60% reduction in resident-related sector and industry emissions over the whole region could reach this goal. On severely polluted days, even a 60% reduction in these two sectors over the whole region was insufficient to satisfy the standard of 75 μg/m3. Moreover, a reduction in traffic emissions resulted in an increase in the O3 concentration. The results of the response surface method showed that PM2.5 in Zhengzhou decreased by 19% in response to the COVID-19 lockdown, which approached the observed reduction of 21%, indicating that the response surface method could be employed to study the impacts of the COVID-19 lockdown on air pollution. This study provides a scientific reference for the formulation of pollution mitigation strategies in the CPER.

REGIONS AND INNOVATION: A NEW TALE OF THREE ECONOMIC REGIONS IN CHINA

Learning is a key component of firm upgrading in emerging economies, and China is no exception to this. Studies have identified, among others, two critical mechanisms that facilitate learning: (1) connections with supportive local governments that enhance access to resources or publicly funded knowledge and (2) connections to co-located foreign multinational enterprises (MNEs) that enhance access to advanced knowledge and capabilities. However, previous studies on the effects of these connections on learning and innovation have had contradictory results. In this study, we develop a model of firm innovation capabilities based on regional differences in firms’ dependence on government and MNEs. Using a sample of 715 indigenous firms from the three historically dominant economic regions in China, we find that the effects of government and MNE ties on local firms’ learning and innovation performance vary depending on the historically dominant dependency patterns in the region.

The unbalanced development and trends of China's regional tourism

In this paper, taking the per capita tourism revenue of the administrative units of Chinese mainland from 2007 to 2018 as an indicator, we analyze the unbalanced regional tourism growth in Chinese mainland and its trends by using kernel density estimation (KDE), the Gini index, and exploratory spatial data analysis (ESDA). The results demonstrate that (1) the distribution of regional tourism data in Chinese mainland and its four economic regions tends to converge, and the relative difference in tourism development among cities is slowly decreasing. (2) The differences in regional tourism development within the four economic regions in China are different, and each part shows its own degree of decline, with the gap in regional tourism development in Western China being the largest, and that in regional tourism growth in Northeast China the smallest. The difference in regional tourism development in both East and Northeast China decreased significantly and that in Central China decreased the least. (3) Finally, the difference in regional tourism development is dominated by a positive spatial correlation. The range of high-high types has expanded, and low-high and high-low types have decreased.

Construction of Spatiotemporal Difference Image of the Impact of Financial Structure on China's Total Factor Productivity.

My country's current research on the influencing factors of total factor productivity has problems such as single evaluation method, low efficiency, and poor overall level in terms of evaluation methods and evaluation efficiency. Based on this, this study divides the financial structure into three traditional sections, banking, securities, and insurance, and uses the DEA model to study the temporal and spatial differences of the financial structure's influence on the total factor productivity of the four major political and economic regions of China's eastern, western, central, and northeastern China. First, establish a DEA model based on data mining algorithms, combine financial data comparisons over the years, to achieve a quantitative analysis of the financial structure's impact on China's total factor productivity, calculate financial efficiency, and then combine the DEA analysis data model with the grey correlation method. Analyze its internal influence rules, and design experiments for model verification analysis. The results show that the DEA analysis model can realize 8 iterations of data on the impact of financial structure on China's total factor productivity, and its evaluation accuracy can reach more than 96.2%.

Clustering analysis of the distribution characteristics of unobserved economic regions in China——Based on multi-indicator panel data

The existence of unobserved economy is one of the important factors affecting GDP calculation. This paper uses the provincial panel data from 2010 to 2019 in China, and adopts the method of principal component feature extraction to carry out cluster analysis on the multi-indicator panel data. This method preserves the dynamic characteristics of the panel data, calculates the comprehensive score of each eigenvalue, and gives weight to the eigenvalue by using the entropy method, so as to optimize the clustering results representing the eight indicators of the unobserved economy. Through the analysis, it is found that the regional development of China’s unobserved economy is obviously different, and each type has different influencing factors. This result has important practical significance for different regions in China to formulate differentiated unobserved economic governance policies. This also helps to make better use of resources and develop an energy-saving economy.

Regions and Innovation: A New Tale of Three Economic Regions in China

Learning is a key component of firm upgrades in emerging economies, and China is no exception. Studies have identified two critical mechanisms that facilitate learning: 1) connections with supportive local governments that enhance access to resources or publicly funded knowledge and 2) connections to foreign MNEs that enhance access to advanced knowledge and capabilities. Previous studies of the effects of these connections on learning and innovation have had contradictory results. In this study, we develop a model of firm innovation capabilities based on regional differences in firms’ dependence on government and MNEs. Using a sample of 715 indigenous firms from the three historically dominant economic regions in China, we find that the impacts of government and MNE ties on local firms’ exploratory learning and innovation performance vary depending on the historically dominant dependency patterns in the region.

Source apportionment of PM2.5 in the most polluted Central Plains Economic Region in China: Implications for joint prevention and control of atmospheric pollution

With continuous strengthening of emission control actions, the air quality in northern China during winter has been improved in recent years. However, the fine particulate matter (PM2.5) concentration remains high in the Central Plains Economic Region (CPER). The Nested Air Quality Prediction Modeling System coupled with an on-line source-tagged model was applied to investigate the source apportionment of primary and secondary PM2.5 in the most polluted area of China from November 2017 to March 2018. On average, local emissions represent the largest contributor to ambient PM2.5 levels, followed by transport from circumjacent cities at five receptors in northern CPER. Under high PM2.5 concentrations (more than 150 μg/m3), 60%–70% of the primary PM2.5 is freshly emitted on the current day, while the contribution of the current day, one day ago, and two or more days ago are comparable to secondary aerosols. The estimation of contributions based on distance shows that the contribution from regions within 200 km reaches more than 80% of the primary PM2.5, while the contribution from long-range transport within 200–500 km still accounts for more than 20% of the secondary PM2.5 at the receptor sites. These results indicate that control measures of primary particle emission should be effective to reduce the primary PM2.5 levels within local and nearby regions, and control measures of gas phase precursors one or more days in advance should be beneficial to surrounding regions within 500 km. The process analysis results of the change in PM2.5 mass over the northern part of the CPER during increased PM2.5 periods illustrate that the existing particle total transport is smaller than the secondary aerosol chemical production and primary aerosol emissions since the westerly and southerly net inputs are mostly offset by the large net outputs at the east boundary in both cases. This finding indicates that the ambient PM2.5 mass in a selected area is strongly affected by the upstream region; meanwhile, the PM2.5 in this area also spreads to the downstream region. Joint prevention and control measures that cooperate with upstream regions for both primary particles and gas phase precursor emissions are crucial for improving the air quality over polluted areas. As the emission control measures would lead to some economic losses, beneficiary downstream regions could reasonably provide certain economic compensation to upstream regions if emergency control measures are taken.

Impact of industrial agglomeration on haze pollution: New evidence from Bohai Sea Economic Region in China

To comply with recent sustainable development trends and initiatives for effectively controlling air pollution problems, this paper applies spatial econometrics and extended STIRPAT model to explore the impact of the industrial agglomeration (IA) on haze pollution (HP) using panel data from critical cities of Bohai Sea economic region (BSER) in China between 2006 and 2018. Influence path characteristics, regional heterogeneity effects and development trends of three major IA on the HP are systematically investigated. The results show that: (1) IA significantly correlated to HP in the BSER. The Siphon Effect in Beijing-Tianjin-Shijiazhuang region plays an important role in the HP and IA, and it weakened with the upgrading of industrial structure and industrial transfer. (2) Both IA and HP have significant spatial autocorrelation properties, the agglomeration and diffusion properties of IA and HP in specific regions are consistent along the time series. (3) However, systematic analysis of various industries indicated that the IA of the primary industry and the secondary industry has a significant positive impact on HP. Interestingly, IA of the third industry has a significant nonlinear effect on HP. Based on the results of the empirical analysis, this study provides insights on the spatial relationship between regional HP and industrial development. This paper gives an effective reference on promoting the distribution of resources and diversified green economic development paths.

Pollution control in urban China: A multi-level analysis on household and industrial pollution

This paper studies the effectiveness of pollution control in urban China by constructing a two sub-system analysis: household pollution control subsystem and industrial pollution control subsystem. We integrate slack-based model with undesirable outputs to estimate the pollution control efficiency in two subsystems for Chinese provinces from 2011 to 2015. The results show strong evidence on provincial and regional heterogeneity in pollution control efficiency for both systems. At provincial level, Beijing, Shanghai, Jiangsu, Guangdong, Hainan, and Qinghai have achieved full efficiency in both household and industrial pollution control compared with other provinces. At regional level, the Eastern Coastal region is the most effective area in environmental protection among eight economic regions in China. We then measure the economic importance of controlling various pollutants by dual price approach. Policy suggestions for each region are given to improve the effectiveness of pollution control in urban China.

Estimation and analysis of municipal food waste and resource utilization potential in China.

With rapid urbanization, municipal food waste (MFW), which is an important part of municipal solid waste, has attracted considerable attention owing to its environmental impact and polluting nature. There has been little research on the quantity and distribution of food waste (FW) produced in China. This study focused on a systematic estimation and analysis of MFW produced in administrative divisions at the prefecture-level and above in China for the first time. From the national level to the prefectural level, with the shrinking of the research units, more intuitive support was obtained for relevant decisions. On the basis of the estimated results, suggestions are provided for proper FW treatment technologies and operational scale of the facilities, and the resource utilization potential has also been estimated. The distribution results indicated that FW characteristics have great variability in the different economic regions of China. Furthermore, it was found that the available FW has a resource utilization potential that is equivalent to 4669.1 million m3 of biogas, 3.6 million tons of biodiesel, and 1.5 million tons of organic fertilizer (dry weight). It is worth mentioning that this amount of biogas can replace 7.5 million tons of standard coal. However, only a small part of the generated MFW can be treated in the existing treatment plants in China. Finally, current key bottlenecks of FW treatment in China have been discussed, and detailed suggestions are presented for further improvement of MFW management.

Characteristics, sources, and reactions of nitrous acid during winter at an urban site in the Central Plains Economic Region in China

Abstract. Nitrous acid (HONO) in the core city of the Central Plains Economic Region in China was measured using an ambient ion monitor from 9 to 31 January 2019. Measurement time intervals were classified into the following periods in accordance with the daily mean values of PM2.5: clean days (CDs), polluted days (PDs), and severely polluted days (SPDs). The HONO concentrations during CD, PD, and SPD periods were 1.2, 2.3, and 3.7 ppbv, respectively. The contributions of the homogeneous reaction, heterogeneous conversion, and direct emissions to HONO sources varied under different pollution levels. The mean values of the net HONO production of the homogeneous reaction (POH+NOnet) in CD, PD, and SPD periods were 0.13, 0.26, and 0.56 ppbv h−1, respectively. The average conversions of NO2 (CHONO) in CD, PD, and SPD periods were 0.72×10-2, 0.64×10-2, and 1.54×10-2 h−1, respectively, indicating that the heterogeneous conversion of NO2 was less important than the homogeneous reaction. Furthermore, the net production of the homogeneous reaction may have been the main factor in the increase of HONO under high-NOx conditions (i.e., when the concentration of NO was higher than that of NO2) at nighttime. Daytime HONO budget analysis showed that the mean values of the unknown source (Punknown) during CD, PD, and SPD periods were 0.26, 0.40, and 1.83 ppbv h−1, respectively. The values of POH+NOnet, CHONO, and Punknown in the SPDs period were comparatively larger than those in other periods, indicating that HONO participated in many reactions. The proportions of nighttime HONO sources also changed during the entire sampling period. Direct emissions and a heterogeneous reaction controlled HONO production in the first half of the night and provided a contribution that is larger than that of the homogeneous reaction. The proportion of homogenization gradually increased in the second half of the night due to the steady increase in NO concentrations. The hourly level of HONO abatement pathways, except for OH + HONO, was at least 0.22 ppbv h−1 in the SPDs period. The cumulative frequency distribution of the HONOemission∕HONO ratio (less than 20 %) was approximately 77 %, which suggested that direct emission was not important. The heterogeneous HONO production increased when the relative humidity (RH) increased, but it decreased when RH increased further. The average HONO∕NOx ratio (4.9 %) was more than twice the assumed globally averaged value (2.0 %).